Generally, feature points (or interest points) are points capable of representing the features of an image, and denote points capable of desirably describing the features of an image or a set of points, regardless of variations in the scale, rotation, or distortion of an image. As feature points, several thousands or several tens of thousands of feature points per picture, for example, may be extracted although they differ depending on the size and content of a given image and the type of feature point extraction/determination method. Such feature points are widely used in the field of image processing or computer vision, and are used in various tasks, such as object recognition, motion tracking, and determination of identicalness between images by, for example, extracting feature points and searching two images for corresponding parts using the feature data of the extracted feature points.
As conventional technology for extracting/determining feature points from a given image, various types of methods have been proposed, including the known methods using maximum/minimum values of the scale space of a Laplacian of Gaussian (LoG) filter or a Difference of Gaussians (DoG) filter. In addition, methods of determining feature points using a determinant of a Hessian matrix are also known.
However, in accordance with the conventional feature point extraction/determination methods, there are many cases where an excessively large number of feature points are obtained from a given image, so that limitations are reached in that the amount of data to be processed in a post-processing procedure becomes excessive, and thus operation time is greatly lengthened. Further, there is a disadvantage in that the amount of feature data formed for each of the extracted/determined feature points and the processing time of the feature data also excessively increase.
Meanwhile, as methods of extracting feature points from an image and forming feature data of the extracted feature points, there are various proposed methods, such as a Scale-Invariant Feature Transform (SIFT) algorithm disclosed in U.S. Pat. No. 6,711,293 (by David G. Lowe) and a Speed Up Robust Features (SURF) algorithm (by H. Bay, T. Tuytelaars and L. van Gool (2006), “SURF: Speeded Up Robust Features”, Proceedings of the 9th European Conference on Computer Vision, Springer LNCS volume 3951, part 1. pp. 404-417). However, since such conventional feature data formation method requires approximately several thousands of several tens-dimensional feature vectors per image, there is a problem in that the operation process is complicated, and the amount of data to be processed is large, so that an excessively long computation time is required, thus causing many problems when a large amount of image data must be processed.